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Hyper-Dimensional Computing Powered DRL for Task Offloading in Edge-Enabled Consumer Electronics
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DOI：10.1109/TNSE.2026.3656226.png)
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Abstract
En 中文
Mobile Edge Computing (MEC) enables the delegation of computing tasks from Consumer Electronics (CEs) to edge servers. This offloading process significantly reduces the latency and energy consumption associated with CEs. Nonetheless, Deep Reinforcement Learning (DRL)-based offloading techniques often encounter challenges in reaching optimal solutions within a confined number of iterations due to the inherent complexity of the task. In light of this challenge, this paper introduces an approach that integrates DRL with Hyper-dimensional Networks (HDN) for task offloading, aiming to improve the efficiency of MEC systems. First, we establish a dynamic model of the MEC system and formulate the task-offloading problem to minimize the cumulative cost incurred by the MEC. Subsequently, we advance an offloading algorithm grounded in HDN principles. The experimental findings demonstrate that DRL with HDN leads to a marked reduction in the computational overhead of MEC systems when contrasted with alternative methodologies. Compared to the baseline algorithm, the proposed HDN-enhanced DRL reduces energy consumption, latency, and system consumption by 10.3%, 14.5%, and 10%, respectively.
Keywords:
Mobile edge computing
consumer electronics
DRL
task offloading
hyper-dimensional computing
Journal
I
IF:
7.9
Papers:
2.5K
Citations:
10.0K
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